All Competitors

Medical vision-language pretraining framework that injects structured medical knowledge into radiology image-text learning for VQA and retrieval.

Pretrained transformer for cell type annotation of scRNA-seq data. Trained on 1.1M cells; outperforms supervised methods on cross-dataset transfer.

RNA foundation model pretrained on 23.7 million non-coding RNA sequences, producing embeddings for structure prediction, annotation, and RNA design.

Motif-oriented DNA pre-training framework that adds motif prediction to an ELECTRA generator-discriminator setup for motif-aware genomic embeddings.

Autoregressive protein language model based on GPT-2 that generates de novo protein sequences sampling unexplored regions of protein space.

Meta AI's family of protein language models scaled to 15B parameters, paired with ESMFold for fast, alignment-free atomic-level structure prediction.

Transformer model for de novo peptide sequencing that reads amino acid sequences directly from tandem mass spectra, with no protein sequence database.

Protein language model family built on CNNs rather than transformers, matching transformer quality while scaling linearly with sequence length.

BERT-based antibody language model pretrained on 57M B cell receptor sequences for paratope prediction and convergent antibody discovery.

RNA language model that learns base-level embeddings capturing sequence context and secondary structure, enabling fast structural alignment.

Protein language model that fuses Gene Ontology knowledge graphs with masked language modeling, improving protein function and interaction prediction.

Protein language model pretrained on UniRef90 with masked language modeling and Gene Ontology annotation prediction, at 16 million parameters.

Antibody-specific language model trained on the OAS database for restoring missing residues and generating high-quality sequence representations.

Multi-modal self-supervised transformer for regulatory genomics, pre-trained on DNA sequence together with transcription factor binding matrices.

Protein complex structure prediction model extending AlphaFold 2 with paired MSA processing and ipTM scoring for multi-chain, multimeric assemblies.

Transformer that predicts gene expression and epigenomic signals from 200kb of DNA sequence, capturing distal enhancers up to 100kb from a promoter.

Suite of six protein language models, including ProtBERT and ProtT5, trained on up to 393 billion amino acids without multiple sequence alignments.

Protein structure prediction model that folds amino acid sequences into 3D structures with atomic accuracy, scoring a median GDT of 92.4 at CASP14.

Protein language model for zero-shot variant effect prediction, scoring mutations by log-odds from evolutionary sequences with no MSA or assay data.

Transformer protein language model trained on 250 million protein sequences that learns structural and functional representations without supervision.

Bidirectional transformer for DNA using k-mer tokenization, fine-tunable for promoter, splice site, and transcription factor binding prediction.

Sparse attention transformer that extends BERT to 8x longer sequences via random, local, and global attention, with genomic sequence applications.

Self-supervised 3D pretrained models for CT and MRI that learn anatomical representations from unlabeled volumes and transfer to segmentation tasks.

Protein language model using a multiplicative LSTM over 24 million UniRef50 sequences to produce fixed-length embeddings for protein engineering.